AI 中文总结
该研究针对巴克斯特-吴模型，采用Novotny-Evertz亚晶格冻结单团簇更新，发现算法重叠作为序参量的临界行为，其幂律衰减指数反映特定动力学而非模型静态性质。
AI 中文摘要
我们通过对巴克斯特-吴（Baxter-Wu）模型进行马尔可夫链蒙特卡罗模拟，研究连续自旋构型的空间重叠。采用Novotny-Evertz亚晶格冻结单团簇更新，追踪临界区域内算法重叠的均值与方差。结果表明，即便在这个三自旋模型中，重叠也可作为遵循相变热力学规律的算法可观测物理量：单团簇重叠均值表现得像序参量，在临界温度Tc处从有限的有序相平台降至零。重叠在临界点处不发散，而是保持有限，且其有限尺寸值随11种尺寸以干净的幂律衰减，U2(Tc) ~ L^(-ψ)，指数ψ=0.378(4)，小于标准Fortuin-Kasteleyn团簇动力学下伊辛（Ising）和Potts模型的≈0.42，表明该指数反映的是Novotny-Evertz亚晶格冻结动力学，而非模型的任何静态性质。
英文摘要
We study the spatial overlap of successive spin configurations generated by Markov chain Monte Carlo simulations of the Baxter-Wu model. Using the Novotny-Evertz sublattice-freezing single-cluster update, we track the mean and variance of the algorithmic overlap across the critical region. We show that, even in this three-spin model, the overlap acts as an algorithmic observable that follows the thermodynamics of the transition: the single-cluster overlap mean behaves like an order parameter, dropping from a finite ordered-phase plateau toward zero across $T_c$. The overlap does not diverge at criticality, instead it remains finite and its finite-size value decays as a clean power law, $U_2(T_c)\sim L^{-ψ}$, over eleven sizes with an exponent $ψ{=}0.378(4)$ smaller than the value $\approx0.42$ found for the Ising and Potts models under standard Fortuin-Kasteleyn cluster dynamics, indicating that it reflects the Novotny-Evertz sublattice-freezing dynamics rather than any static property of the model.
Comments8 pages, 12 figures
